In [ ]:
#Importing useful libraries for data visualizations
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

#Connecting to Google Drive
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive
In [ ]:
#Connection to Dataset
path = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/application_train.csv"
path1 = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/credit_card_balance.csv"
path2 = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/application_test.csv"
path3 = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/bureau_balance.csv"
path4 = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/bureau.csv"
path5 = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/HomeCredit_columns_description.csv"
path6 = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/installments_payments.csv"
path7 = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/POS_CASH_balance.csv"
path8 = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/previous_application.csv"
path9 = "/content/drive/MyDrive/AI POCs/Datasets/Fraud Detection Datasets/sample_submission.csv"

app_train = pd.read_csv(path)
app_test = pd.read_csv(path2)
credit = pd.read_csv(path1)
bureau_balance = pd.read_csv(path3)
bureau = pd.read_csv(path4)
#descriptions = pd.read_csv(path5)
installments = pd.read_csv(path6)
POS = pd.read_csv(path7)
prev_app = pd.read_csv(path8)
sample = pd.read_csv(path9)
In [ ]:
app_train.head()
Out[ ]:
SK_ID_CURR TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY ... FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK AMT_REQ_CREDIT_BUREAU_MON AMT_REQ_CREDIT_BUREAU_QRT AMT_REQ_CREDIT_BUREAU_YEAR
0 100002 1 Cash loans M N Y 0 202500.0 406597.5 24700.5 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
1 100003 0 Cash loans F N N 0 270000.0 1293502.5 35698.5 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
2 100004 0 Revolving loans M Y Y 0 67500.0 135000.0 6750.0 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
3 100006 0 Cash loans F N Y 0 135000.0 312682.5 29686.5 ... 0 0 0 0 NaN NaN NaN NaN NaN NaN
4 100007 0 Cash loans M N Y 0 121500.0 513000.0 21865.5 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 122 columns

In [ ]:
app_test.head()
Out[ ]:
SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE ... FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK AMT_REQ_CREDIT_BUREAU_MON AMT_REQ_CREDIT_BUREAU_QRT AMT_REQ_CREDIT_BUREAU_YEAR
0 100001 Cash loans F N Y 0 135000.0 568800.0 20560.5 450000.0 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
1 100005 Cash loans M N Y 0 99000.0 222768.0 17370.0 180000.0 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
2 100013 Cash loans M Y Y 0 202500.0 663264.0 69777.0 630000.0 ... 0 0 0 0 0.0 0.0 0.0 0.0 1.0 4.0
3 100028 Cash loans F N Y 2 315000.0 1575000.0 49018.5 1575000.0 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
4 100038 Cash loans M Y N 1 180000.0 625500.0 32067.0 625500.0 ... 0 0 0 0 NaN NaN NaN NaN NaN NaN

5 rows × 121 columns

In [ ]:
credit.head()
Out[ ]:
SK_ID_PREV SK_ID_CURR MONTHS_BALANCE AMT_BALANCE AMT_CREDIT_LIMIT_ACTUAL AMT_DRAWINGS_ATM_CURRENT AMT_DRAWINGS_CURRENT AMT_DRAWINGS_OTHER_CURRENT AMT_DRAWINGS_POS_CURRENT AMT_INST_MIN_REGULARITY ... AMT_RECIVABLE AMT_TOTAL_RECEIVABLE CNT_DRAWINGS_ATM_CURRENT CNT_DRAWINGS_CURRENT CNT_DRAWINGS_OTHER_CURRENT CNT_DRAWINGS_POS_CURRENT CNT_INSTALMENT_MATURE_CUM NAME_CONTRACT_STATUS SK_DPD SK_DPD_DEF
0 2562384 378907 -6 56.970 135000 0.0 877.5 0.0 877.5 1700.325 ... 0.000 0.000 0.0 1 0.0 1.0 35.0 Active 0 0
1 2582071 363914 -1 63975.555 45000 2250.0 2250.0 0.0 0.0 2250.000 ... 64875.555 64875.555 1.0 1 0.0 0.0 69.0 Active 0 0
2 1740877 371185 -7 31815.225 450000 0.0 0.0 0.0 0.0 2250.000 ... 31460.085 31460.085 0.0 0 0.0 0.0 30.0 Active 0 0
3 1389973 337855 -4 236572.110 225000 2250.0 2250.0 0.0 0.0 11795.760 ... 233048.970 233048.970 1.0 1 0.0 0.0 10.0 Active 0 0
4 1891521 126868 -1 453919.455 450000 0.0 11547.0 0.0 11547.0 22924.890 ... 453919.455 453919.455 0.0 1 0.0 1.0 101.0 Active 0 0

5 rows × 23 columns

In [ ]:
bureau_balance.head()
Out[ ]:
SK_ID_BUREAU MONTHS_BALANCE STATUS
0 5715448 0 C
1 5715448 -1 C
2 5715448 -2 C
3 5715448 -3 C
4 5715448 -4 C
In [ ]:
bureau.head()
Out[ ]:
SK_ID_CURR SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY DAYS_CREDIT CREDIT_DAY_OVERDUE DAYS_CREDIT_ENDDATE DAYS_ENDDATE_FACT AMT_CREDIT_MAX_OVERDUE CNT_CREDIT_PROLONG AMT_CREDIT_SUM AMT_CREDIT_SUM_DEBT AMT_CREDIT_SUM_LIMIT AMT_CREDIT_SUM_OVERDUE CREDIT_TYPE DAYS_CREDIT_UPDATE AMT_ANNUITY
0 215354 5714462 Closed currency 1 -497 0 -153.0 -153.0 NaN 0 91323.0 0.0 NaN 0.0 Consumer credit -131 NaN
1 215354 5714463 Active currency 1 -208 0 1075.0 NaN NaN 0 225000.0 171342.0 NaN 0.0 Credit card -20 NaN
2 215354 5714464 Active currency 1 -203 0 528.0 NaN NaN 0 464323.5 NaN NaN 0.0 Consumer credit -16 NaN
3 215354 5714465 Active currency 1 -203 0 NaN NaN NaN 0 90000.0 NaN NaN 0.0 Credit card -16 NaN
4 215354 5714466 Active currency 1 -629 0 1197.0 NaN 77674.5 0 2700000.0 NaN NaN 0.0 Consumer credit -21 NaN
In [ ]:
installments.head()
Out[ ]:
SK_ID_PREV SK_ID_CURR NUM_INSTALMENT_VERSION NUM_INSTALMENT_NUMBER DAYS_INSTALMENT DAYS_ENTRY_PAYMENT AMT_INSTALMENT AMT_PAYMENT
0 1054186 161674 1.0 6 -1180.0 -1187.0 6948.360 6948.360
1 1330831 151639 0.0 34 -2156.0 -2156.0 1716.525 1716.525
2 2085231 193053 2.0 1 -63.0 -63.0 25425.000 25425.000
3 2452527 199697 1.0 3 -2418.0 -2426.0 24350.130 24350.130
4 2714724 167756 1.0 2 -1383.0 -1366.0 2165.040 2160.585
In [ ]:
POS.head()
Out[ ]:
SK_ID_PREV SK_ID_CURR MONTHS_BALANCE CNT_INSTALMENT CNT_INSTALMENT_FUTURE NAME_CONTRACT_STATUS SK_DPD SK_DPD_DEF
0 1803195 182943 -31 48.0 45.0 Active 0 0
1 1715348 367990 -33 36.0 35.0 Active 0 0
2 1784872 397406 -32 12.0 9.0 Active 0 0
3 1903291 269225 -35 48.0 42.0 Active 0 0
4 2341044 334279 -35 36.0 35.0 Active 0 0
In [ ]:
prev_app.head()
Out[ ]:
SK_ID_PREV SK_ID_CURR NAME_CONTRACT_TYPE AMT_ANNUITY AMT_APPLICATION AMT_CREDIT AMT_DOWN_PAYMENT AMT_GOODS_PRICE WEEKDAY_APPR_PROCESS_START HOUR_APPR_PROCESS_START ... NAME_SELLER_INDUSTRY CNT_PAYMENT NAME_YIELD_GROUP PRODUCT_COMBINATION DAYS_FIRST_DRAWING DAYS_FIRST_DUE DAYS_LAST_DUE_1ST_VERSION DAYS_LAST_DUE DAYS_TERMINATION NFLAG_INSURED_ON_APPROVAL
0 2030495 271877 Consumer loans 1730.430 17145.0 17145.0 0.0 17145.0 SATURDAY 15 ... Connectivity 12.0 middle POS mobile with interest 365243.0 -42.0 300.0 -42.0 -37.0 0.0
1 2802425 108129 Cash loans 25188.615 607500.0 679671.0 NaN 607500.0 THURSDAY 11 ... XNA 36.0 low_action Cash X-Sell: low 365243.0 -134.0 916.0 365243.0 365243.0 1.0
2 2523466 122040 Cash loans 15060.735 112500.0 136444.5 NaN 112500.0 TUESDAY 11 ... XNA 12.0 high Cash X-Sell: high 365243.0 -271.0 59.0 365243.0 365243.0 1.0
3 2819243 176158 Cash loans 47041.335 450000.0 470790.0 NaN 450000.0 MONDAY 7 ... XNA 12.0 middle Cash X-Sell: middle 365243.0 -482.0 -152.0 -182.0 -177.0 1.0
4 1784265 202054 Cash loans 31924.395 337500.0 404055.0 NaN 337500.0 THURSDAY 9 ... XNA 24.0 high Cash Street: high NaN NaN NaN NaN NaN NaN

5 rows × 37 columns

In [ ]:
app_train.shape
Out[ ]:
(307511, 122)
In [ ]:
app_test.shape
Out[ ]:
(48744, 121)
In [ ]:
print(app_train.columns)

Index(['SK_ID_CURR', 'TARGET', 'NAME_CONTRACT_TYPE', 'CODE_GENDER',
       'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',
       'AMT_CREDIT', 'AMT_ANNUITY',
       ...
       'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',
       'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR',
       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
       'AMT_REQ_CREDIT_BUREAU_YEAR'],
      dtype='object', length=122)
In [ ]:
numeric_apptrain = app_train.select_dtypes(include=['float64', 'int64'])
categorical_apptrain = app_train.select_dtypes(include=['object'])
In [ ]:
numeric_apptrain.hist(figsize=(15,12), bins=30, edgecolor='black')
plt.suptitle("Distirbution of Numerical Features")
plt.tight_layout()
plt.show()
No description has been provided for this image
In [ ]:
def plot_correlation_heatmap(app_train):

    plt.figure(figsize=(55, 40))
    correlation_matrix = numeric_apptrain.corr()
    sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm', square=True)
    plt.title('Correlation Heatmap')
    plt.show()

plot_correlation_heatmap(app_train)
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In [ ]:
def plot_selected_correlation_heatmap(app_train):
    # Select the relevant columns
    selected_columns = [
        'APARTMENTS_AVG',
        'BASEMENTAREA_AVG',
        'YEARS_BEGINEXPLUATATION_AVG',
        'YEARS_BUILD_AVG',
        'COMMONAREA_AVG',
        'ELEVATORS_AVG',
        'ENTRANCES_AVG',
        'FLOORSMAX_AVG',
        'FLOORSMIN_AVG',
        'LANDAREA_AVG',
        'LIVINGAPARTMENTS_AVG',
        'LIVINGAREA_AVG',
        'NONLIVINGAPARTMENTS_AVG',
        'NONLIVINGAREA_AVG',
        'APARTMENTS_MODE',
        'BASEMENTAREA_MODE',
        'YEARS_BEGINEXPLUATATION_MODE',
        'YEARS_BUILD_MODE',
        'COMMONAREA_MODE',
        'ELEVATORS_MODE',
        'ENTRANCES_MODE',
        'FLOORSMAX_MODE',
        'FLOORSMIN_MODE',
        'LANDAREA_MODE',
        'LIVINGAPARTMENTS_MODE',
        'LIVINGAREA_MODE',
        'NONLIVINGAPARTMENTS_MODE',
        'NONLIVINGAREA_MODE',
        'APARTMENTS_MEDI',
        'BASEMENTAREA_MEDI',
        'YEARS_BEGINEXPLUATATION_MEDI',
        'YEARS_BUILD_MEDI',
        'COMMONAREA_MEDI',
        'ELEVATORS_MEDI',
        'ENTRANCES_MEDI',
        'FLOORSMAX_MEDI',
        'FLOORSMIN_MEDI',
        'LANDAREA_MEDI',
        'LIVINGAPARTMENTS_MEDI',
        'LIVINGAREA_MEDI',
        'NONLIVINGAPARTMENTS_MEDI',
        'NONLIVINGAREA_MEDI',
        'TOTALAREA_MODE'
    ]

    # Create the correlation matrix for the selected columns
    correlation_matrix = app_train[selected_columns].corr()

    # Plot the heatmap
    plt.figure(figsize=(30, 25))  # Adjust size as needed
    sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm', square=True)
    plt.title('Correlation Heatmap for Selected Columns', fontsize=24)
    plt.show()

plot_selected_correlation_heatmap(app_train)
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In [ ]:
remaining_columns = [
    'APARTMENTS_AVG',
    'BASEMENTAREA_AVG',
    'YEARS_BEGINEXPLUATATION_AVG',
    'YEARS_BUILD_AVG',
    'COMMONAREA_AVG',
    'ENTRANCES_AVG',
    'FLOORSMAX_AVG',
    'FLOORSMIN_AVG',
    'LANDAREA_AVG',
    'NONLIVINGAPARTMENTS_AVG',
    'NONLIVINGAREA_AVG',
    'BASEMENTAREA_MODE',
    'YEARS_BEGINEXPLUATATION_MODE',
    'YEARS_BUILD_MODE',
    'COMMONAREA_MODE',
    'ENTRANCES_MODE',
    'FLOORSMAX_MODE',
    'FLOORSMIN_MODE',
    'LANDAREA_MODE',
    'TOTALAREA_MODE'
]

# Select the remaining columns
filtered_data = app_train[remaining_columns]

# Calculate the correlation matrix
correlation_matrix = filtered_data.corr()

# Plot the heatmap
plt.figure(figsize=(16, 12))
sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm', square=True)
plt.title('Correlation Heatmap of Remaining Columns')
plt.show()
No description has been provided for this image
In [ ]:
threshold = numeric_apptrain.var().quantile(0.15)
low_variance_features = numeric_apptrain.var()[numeric_apptrain.var() < threshold]
print(low_variance_features)

REGION_POPULATION_RELATIVE    0.000191
FLAG_MOBIL                    0.000003
FLAG_CONT_MOBILE              0.001863
NONLIVINGAPARTMENTS_AVG       0.002278
NONLIVINGAPARTMENTS_MODE      0.002141
NONLIVINGAPARTMENTS_MEDI      0.002248
FLAG_DOCUMENT_2               0.000042
FLAG_DOCUMENT_4               0.000081
FLAG_DOCUMENT_7               0.000192
FLAG_DOCUMENT_10              0.000023
FLAG_DOCUMENT_12              0.000007
FLAG_DOCUMENT_15              0.001208
FLAG_DOCUMENT_17              0.000267
FLAG_DOCUMENT_19              0.000595
FLAG_DOCUMENT_20              0.000507
FLAG_DOCUMENT_21              0.000335
dtype: float64
In [ ]:
numeric_bureau_balance = bureau_balance.select_dtypes(include=['float64', 'int64'])
categorical_bureau_balance = bureau_balance.select_dtypes(include=['object'])
In [ ]:
bureau_balance.shape
Out[ ]:
(27299925, 3)
In [ ]:
numeric_bureau_balance.hist(figsize=(15,12), bins=30, edgecolor='black')
plt.suptitle("Distirbution of Numerical Features")
plt.tight_layout()
plt.show()
No description has been provided for this image
In [ ]:
bureau_balance.columns
Out[ ]:
Index(['SK_ID_BUREAU', 'MONTHS_BALANCE', 'STATUS'], dtype='object')
In [ ]:
bureau.columns
Out[ ]:
Index(['SK_ID_CURR', 'SK_ID_BUREAU', 'CREDIT_ACTIVE', 'CREDIT_CURRENCY',
       'DAYS_CREDIT', 'CREDIT_DAY_OVERDUE', 'DAYS_CREDIT_ENDDATE',
       'DAYS_ENDDATE_FACT', 'AMT_CREDIT_MAX_OVERDUE', 'CNT_CREDIT_PROLONG',
       'AMT_CREDIT_SUM', 'AMT_CREDIT_SUM_DEBT', 'AMT_CREDIT_SUM_LIMIT',
       'AMT_CREDIT_SUM_OVERDUE', 'CREDIT_TYPE', 'DAYS_CREDIT_UPDATE',
       'AMT_ANNUITY'],
      dtype='object')
In [ ]:
numeric_bureau = bureau.select_dtypes(include=['float64', 'int64'])
categorical_bureau = bureau.select_dtypes(include=['object'])
In [ ]:
def plot_correlation_heatmap(bureau):

    plt.figure(figsize=(16, 12))
    correlation_matrix = numeric_bureau.corr()
    sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm', square=True)
    plt.title('Correlation Heatmap')
    plt.show()

plot_correlation_heatmap(bureau)
No description has been provided for this image

Will probably drop Day_Credit_Update

In [ ]:
bureau.isnull().sum()
Out[ ]:
0
SK_ID_CURR 0
SK_ID_BUREAU 0
CREDIT_ACTIVE 0
CREDIT_CURRENCY 0
DAYS_CREDIT 0
CREDIT_DAY_OVERDUE 0
DAYS_CREDIT_ENDDATE 105553
DAYS_ENDDATE_FACT 633653
AMT_CREDIT_MAX_OVERDUE 1124488
CNT_CREDIT_PROLONG 0
AMT_CREDIT_SUM 13
AMT_CREDIT_SUM_DEBT 257669
AMT_CREDIT_SUM_LIMIT 591780
AMT_CREDIT_SUM_OVERDUE 0
CREDIT_TYPE 0
DAYS_CREDIT_UPDATE 0
AMT_ANNUITY 1226791

In [ ]:
threshold = numeric_bureau.var().quantile(0.15)
low_variance_features = numeric_bureau.var()[numeric_bureau.var() < threshold]
print(low_variance_features)

CREDIT_DAY_OVERDUE    1335.495218
CNT_CREDIT_PROLONG       0.009259
dtype: float64
In [ ]:
numeric_installments = installments.select_dtypes(include=['float64', 'int64'])
categorical_installments = installments.select_dtypes(include=['object'])
In [ ]:
def plot_correlation_heatmap(bureau):

    plt.figure(figsize=(16, 12))
    correlation_matrix = numeric_installments.corr()
    sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm', square=True)
    plt.title('Correlation Heatmap')
    plt.show()

plot_correlation_heatmap(bureau)
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In [ ]:
installments.isnull().sum()
Out[ ]:
0
SK_ID_PREV 0
SK_ID_CURR 0
NUM_INSTALMENT_VERSION 0
NUM_INSTALMENT_NUMBER 0
DAYS_INSTALMENT 0
DAYS_ENTRY_PAYMENT 2905
AMT_INSTALMENT 0
AMT_PAYMENT 2905

In [ ]:
threshold = numeric_installments.var().quantile(0.15)
low_variance_features = numeric_installments.var()[numeric_installments.var() < threshold]
print(low_variance_features)

NUM_INSTALMENT_VERSION      1.071672
NUM_INSTALMENT_NUMBER     710.972463
dtype: float64
In [ ]:
POS.isnull().sum()
Out[ ]:
0
SK_ID_PREV 0
SK_ID_CURR 0
MONTHS_BALANCE 0
CNT_INSTALMENT 26071
CNT_INSTALMENT_FUTURE 26087
NAME_CONTRACT_STATUS 0
SK_DPD 0
SK_DPD_DEF 0

In [ ]:
numeric_POS = POS.select_dtypes(include=['float64', 'int64'])
categorical_POS = POS.select_dtypes(include=['object'])
In [ ]:
def plot_correlation_heatmap(POS):

    plt.figure(figsize=(16, 12))
    correlation_matrix = numeric_POS.corr()
    sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm', square=True)
    plt.title('Correlation Heatmap')
    plt.show()

plot_correlation_heatmap(POS)
No description has been provided for this image
In [ ]:
threshold = numeric_POS.var().quantile(0.15)
low_variance_features = numeric_POS.var()[numeric_POS.var() < threshold]
print(low_variance_features)

CNT_INSTALMENT_FUTURE    123.411162
dtype: float64
In [ ]:
numeric_prev_app = prev_app.select_dtypes(include=['float64', 'int64'])
categorical_prev_app = prev_app.select_dtypes(include=['object'])
In [ ]:
def plot_correlation_heatmap(prev_app):

    plt.figure(figsize=(16, 12))
    correlation_matrix = numeric_prev_app.corr()
    sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm', square=True)
    plt.title('Correlation Heatmap')
    plt.show()

plot_correlation_heatmap(prev_app)
No description has been provided for this image

Drop days_last_due,NFLAG_LAST_APPL_IN_DAY and RATE_INTEREST_PRIMARY, AMT_GOODS_PRICE, AMT_APPLICATION

In [ ]:
threshold = numeric_prev_app.var().quantile(0.15)
low_variance_features = numeric_prev_app.var()[numeric_prev_app.var() < threshold]
print(low_variance_features)

NFLAG_LAST_APPL_IN_DAY      0.003520
RATE_INTEREST_PRIMARY       0.007686
RATE_INTEREST_PRIVILEGED    0.010176
dtype: float64
In [ ]:
numeric_prev_app.columns
Out[ ]:
Index(['SK_ID_PREV', 'SK_ID_CURR', 'AMT_ANNUITY', 'AMT_APPLICATION',
       'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
       'HOUR_APPR_PROCESS_START', 'NFLAG_LAST_APPL_IN_DAY',
       'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
       'RATE_INTEREST_PRIVILEGED', 'DAYS_DECISION', 'SELLERPLACE_AREA',
       'CNT_PAYMENT', 'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE',
       'DAYS_LAST_DUE_1ST_VERSION', 'DAYS_LAST_DUE', 'DAYS_TERMINATION',
       'NFLAG_INSURED_ON_APPROVAL'],
      dtype='object')
In [ ]:
credit.fillna(0, inplace=True)
credit.isnull().sum()
Out[ ]:
0
SK_ID_PREV 0
SK_ID_CURR 0
MONTHS_BALANCE 0
AMT_BALANCE 0
AMT_CREDIT_LIMIT_ACTUAL 0
AMT_DRAWINGS_ATM_CURRENT 0
AMT_DRAWINGS_CURRENT 0
AMT_DRAWINGS_OTHER_CURRENT 0
AMT_DRAWINGS_POS_CURRENT 0
AMT_INST_MIN_REGULARITY 0
AMT_PAYMENT_CURRENT 0
AMT_PAYMENT_TOTAL_CURRENT 0
AMT_RECEIVABLE_PRINCIPAL 0
AMT_RECIVABLE 0
AMT_TOTAL_RECEIVABLE 0
CNT_DRAWINGS_ATM_CURRENT 0
CNT_DRAWINGS_CURRENT 0
CNT_DRAWINGS_OTHER_CURRENT 0
CNT_DRAWINGS_POS_CURRENT 0
CNT_INSTALMENT_MATURE_CUM 0
NAME_CONTRACT_STATUS 0
SK_DPD 0
SK_DPD_DEF 0

In [ ]:
numeric_credit = credit.select_dtypes(include=['float64', 'int64'])
categorical_credit = credit.select_dtypes(include=['object'])
In [ ]:
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

scaler = StandardScaler()
data_scaled = scaler.fit_transform(numeric_credit)
In [ ]:
pca = PCA()
pca.fit(data_scaled)
Out[ ]:
PCA()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
PCA()
In [ ]:
principal_components = pca.transform(data_scaled)
In [ ]:
explained_variance = pca.explained_variance_ratio_
In [ ]:
print("Explained Variance Ratio:", explained_variance)

Explained Variance Ratio: [2.77723548e-01 1.26997831e-01 8.25123509e-02 7.34888191e-02
 6.99592974e-02 5.61109774e-02 4.80939100e-02 4.66181209e-02
 4.55723902e-02 4.51797401e-02 3.49052653e-02 3.21431533e-02
 2.49051725e-02 1.94195985e-02 1.31174246e-02 2.89572204e-03
 2.28499310e-04 1.11794413e-04 1.24865154e-05 3.77273407e-06
 1.26576661e-07 0.00000000e+00]
In [ ]:
plt.figure(figsize=(10, 6))
plt.plot(range(1, len(explained_variance) + 1), explained_variance, marker='o')
plt.title('Explained Variance by Principal Components')
plt.xlabel('Principal Component')
plt.ylabel('Explained Variance Ratio')
plt.show()
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